{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Getting Started with MLRUN\n",
    "----------------------------"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id='top'></a>\n",
    "### **Understanding functions and running tasks locally**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**[intall mlrun](#install)**<br>\n",
    "**[mlrun setup](#setup)**<br>\n",
    "**[create and run a local function](#create-local)**<br>\n",
    "**[create a new mlrun Task and run it](#create-new-task)**<br>\n",
    "**[inspecting the run results and outputs](#inspecting)**<br>\n",
    "**[using hyperparameter tasks](#using-hyperparamter-tasks)**<br>\n",
    "**[running Task's through the cli](#tasks-cli)**<br>\n",
    "**[inline code and running on multiple runtimes](#inline)**<br>\n",
    "**[running locally in the notebook](#run-locally)**<br>\n",
    "**[hyper parameters taken from a csv file](#run-csv)**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id=\"install\" ></a>\n",
    "______________________________________________"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# **install**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Uncomment this to install mlrun package, restart the kernel after\n",
    "\n",
    "# !pip install mlrun"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id=\"setup\"></a>\n",
    "______________________________________________"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# **mlrun setup**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "MLRun tracks jobs and artifacts, collecting metadata in local file directory or in a DB.\n",
    "\n",
    "The DB/API path can be set using the environment variable ```MLRUN_DBPATH``` or the config object ```mlconf.dbpath```, we will try and get it from the environment.\n",
    "\n",
    "**Note:** for _distributed jobs_ and and an _interactive UI_ you must use the `mlrun-api` service (and not the file DB)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For a local file DB, in the current folder:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "from mlrun import run_local, new_task, mlconf\n",
    "from os import path\n",
    "\n",
    "mlconf.dbpath = mlconf.dbpath or \"./\""
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For the ```mlrun-api``` service (in Kubernetes) use:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# uncomment for working with the DB\n",
    "# mlconf.dbpath = mlconf.dbpath or 'http://mlrun-api:8080'"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id=\"create-local\"></a>\n",
    "______________________________________________"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# **create a new mlrun task and submit it to a local function**\n",
    "\n",
    "An mlrun ```Task``` defines job inputs/outputs and metadata:\n",
    "* input parameters\n",
    "* hyper-parameters (or parameter files)\n",
    "* input datasets\n",
    "* default paths (for input/output)\n",
    "* secrets (job credentials)\n",
    "* ```Task``` metadata: name, project, labels, etc.\n",
    "\n",
    "If a function supports multiple handlers (another term for method, function), we also need to define the specific handler (or set the `spec.default_handler`).\n",
    "\n",
    "`Task` object have helper methods like `.with_params()`, `.with_secrets()`, `.with_input()`, `.set_label()` for conviniance.\n",
    "\n",
    "This example shows how we can create a new task with various parameters and later use `.run()` to submit the task to our new function.<br>\n",
    "\n",
    "artifacts from each run are stored in the `artifact_path` which can be set globally through environment var (`MLRUN_ARTIFACT_PATH`) or through the config, if its not already set we can create a directory and use it in our runs. Using `{{run.uid}}` in the path will allow us to create a unique directory per run, when we use pipelines we can use the `{{workflow.uid}}` template option.\n",
    "\n",
    "> Note: artifact path can be a local path or a URL (starts with s3://, v3io://, etc.), if we want the artifacts to show in the UI the artifact path must be on a shared file or object media and should not be a relative path, on Iguazio platform the notebooks are always on the shared file system."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "out = mlconf.artifact_path or path.abspath(\"./data\")\n",
    "# {{run.uid}} will be substituted with the run id, so output will be written to different directoried per run\n",
    "artifact_path = path.join(out, \"{{run.uid}}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Then we create a new task and set its properties using helper methods:<br>\n",
    "The [secrets `file`](secrets.txt) is a list of key=value properties "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "task = (\n",
    "    new_task(name=\"demo\", params={\"p1\": 5}, artifact_path=artifact_path)\n",
    "    .with_secrets(\"file\", \"secrets.txt\")\n",
    "    .set_label(\"type\", \"demo\")\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id=\"create-new-task\"></a>\n",
    "______________________________________________"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### **run local code**\n",
    "The following example creates a temp local function mapped to the **[training.py](training.py)** code file (located in the same folder as this notebook) and run it."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "mlrun supports multiple _**runtimes**_ (handler, local, nuclio, job, spark, mpi, etc., see **[supported runtimes](https://github.com/mlrun/mlrun/tree/master/mlrun/runtimes)** for more details). _**local**_ runtime runs code in your local/notebook environment."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[mlrun] 2020-06-08 16:40:39,061 starting run demo uid=e9bdd9983067436eb080d9e61a458e4a  -> http://mlrun-api:8080\n",
      "[mlrun] 2020-06-08 16:40:39,093 starting local run: training.py # main\n",
      "[mlrun] 2020-06-08 16:40:41,607 logging run results to: http://mlrun-api:8080\n",
      "Run: demo (uid=e9bdd9983067436eb080d9e61a458e4a)\n",
      "Params: p1=5, p2=a-string\n",
      "accesskey = 456\n",
      "file\n",
      "b\"I'm a local input file\\n\"\n",
      "\n",
      "[mlrun] 2020-06-08 16:40:41,679 log artifact model at /User/ml3/mlrun/examples/data/e9bdd9983067436eb080d9e61a458e4a/model.txt, size: 10, db: Y\n",
      "[mlrun] 2020-06-08 16:40:41,702 log artifact html_result at /User/ml3/mlrun/examples/data/e9bdd9983067436eb080d9e61a458e4a/result.html, size: 17, db: Y\n",
      "[mlrun] 2020-06-08 16:40:41,725 log artifact dataset at /User/ml3/mlrun/examples/data/e9bdd9983067436eb080d9e61a458e4a/dataset.csv, size: 12, db: Y\n",
      "[mlrun] 2020-06-08 16:40:41,748 log artifact chart at /User/ml3/mlrun/examples/data/e9bdd9983067436eb080d9e61a458e4a/chart.html, size: 881, db: Y\n",
      "[mlrun] 2020-06-08 16:40:41,807 log artifact mydf at /User/ml3/mlrun/examples/data/e9bdd9983067436eb080d9e61a458e4a/mydf.csv, size: 135, db: Y\n",
      "\n"
     ]
    },
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       "      iframe.setAttribute(\"srcdoc\", this.responseText);\n",
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       "    console.log(this.responseText);\n",
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       "\n",
       "  const oReq = new XMLHttpRequest();\n",
       "  oReq.addEventListener(\"load\", reqListener);\n",
       "  oReq.open(\"GET\", el.title);\n",
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       "  //iframe.src = el.title;\n",
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       "  if (resultPane.classList.contains(\"hidden\")) {\n",
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       "function closePanel(el) {\n",
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       "\n",
       "</script>\n",
       "<div class=\"master-wrapper\">\n",
       "  <div class=\"block master-tbl\"><div>\n",
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       "\n",
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       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>project</th>\n",
       "      <th>uid</th>\n",
       "      <th>iter</th>\n",
       "      <th>start</th>\n",
       "      <th>state</th>\n",
       "      <th>name</th>\n",
       "      <th>labels</th>\n",
       "      <th>inputs</th>\n",
       "      <th>parameters</th>\n",
       "      <th>results</th>\n",
       "      <th>artifacts</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>default</td>\n",
       "      <td><div title=\"e9bdd9983067436eb080d9e61a458e4a\"><a href=\"https://mlrun-ui.default-tenant.app.yh55.iguazio-cd2.com/projects/default/jobs/e9bdd9983067436eb080d9e61a458e4a/info\" target=\"_blank\" >...1a458e4a</a></div></td>\n",
       "      <td>0</td>\n",
       "      <td>Jun 08 16:40:41</td>\n",
       "      <td>completed</td>\n",
       "      <td>demo</td>\n",
       "      <td><div class=\"dictlist\">type=demo</div><div class=\"dictlist\">v3io_user=admin</div><div class=\"dictlist\">kind=</div><div class=\"dictlist\">owner=admin</div><div class=\"dictlist\">host=jupyter-65887d7ffb-5jsn2</div><div class=\"dictlist\">framework=sklearn</div></td>\n",
       "      <td><div class=\"artifact\" onclick=\"expandPanel(this)\" paneName=\"result88d47f47\" title=\"/files/ml3/mlrun/examples/infile.txt\">infile.txt</div></td>\n",
       "      <td><div class=\"dictlist\">p1=5</div><div class=\"dictlist\">p2=a-string</div></td>\n",
       "      <td><div class=\"dictlist\">accuracy=10</div><div class=\"dictlist\">loss=15</div></td>\n",
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       "    <div class=\"pane-header\">\n",
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       "</div>\n"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "to track results use .show() or .logs() or in CLI: \n",
      "!mlrun get run e9bdd9983067436eb080d9e61a458e4a --project default , !mlrun logs e9bdd9983067436eb080d9e61a458e4a --project default\n",
      "[mlrun] 2020-06-08 16:40:42,215 run executed, status=completed\n"
     ]
    }
   ],
   "source": [
    "# run our task using our new function\n",
    "run_object = run_local(task, command=\"training.py\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<b>Hover over the inputs/artifacts to see full link, or click to see the content !!!</b>\n",
    "<a id=\"inspecting\"></a>\n",
    "______________________________________________"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# **inspecting the run results and outputs**\n",
    "\n",
    "Every ```run``` object (the result of a `.run()` method) has the following properties and methods:\n",
    "* `.uid()`   - return the unique id\n",
    "* `.state()` - return the last known state\n",
    "* `.show()`  - show the latest task state and data in a visual widget (with hyper links and hints)\n",
    "* `.outputs` - return a dict of the run results and artifact paths\n",
    "* `.logs()`  - return the latest logs, use `Watch=False` to disable interactive mode in running tasks\n",
    "* `.artifact(key)` - return full artifact details\n",
    "* `.output(key)`   - return specific result or artifact (path)\n",
    "* `.to_dict()`, `.to_yaml()`, `.to_json()` - convert the run object to dict/yaml/json"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'e9bdd9983067436eb080d9e61a458e4a'"
      ]
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     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "run_object.uid()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'spec': {'parameters': {'p1': 5, 'p2': 'a-string'},\n",
       "  'inputs': {'infile.txt': 'infile.txt'},\n",
       "  'outputs': [],\n",
       "  'output_path': '/User/ml3/mlrun/examples/data/e9bdd9983067436eb080d9e61a458e4a',\n",
       "  'function': 'default/training',\n",
       "  'secret_sources': [],\n",
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       "  'labels': {'type': 'demo',\n",
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       "  'results': {'accuracy': 10, 'loss': 15},\n",
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       "    'iter': 0,\n",
       "    'tree': 'e9bdd9983067436eb080d9e61a458e4a',\n",
       "    'src_path': 'model.txt',\n",
       "    'target_path': '/User/ml3/mlrun/examples/data/e9bdd9983067436eb080d9e61a458e4a/model.txt',\n",
       "    'hash': '8170b9a53bbb1f4d52733bc6824955e3a362d4a1',\n",
       "    'size': 10,\n",
       "    'db_key': 'demo_model'},\n",
       "   {'key': 'html_result',\n",
       "    'kind': '',\n",
       "    'iter': 0,\n",
       "    'tree': 'e9bdd9983067436eb080d9e61a458e4a',\n",
       "    'src_path': 'result.html',\n",
       "    'target_path': '/User/ml3/mlrun/examples/data/e9bdd9983067436eb080d9e61a458e4a/result.html',\n",
       "    'hash': '11f50bed0aa902cbdcf3249c610af88bc26f1619',\n",
       "    'size': 17,\n",
       "    'db_key': 'demo_html_result'},\n",
       "   {'key': 'dataset',\n",
       "    'kind': 'table',\n",
       "    'iter': 0,\n",
       "    'tree': 'e9bdd9983067436eb080d9e61a458e4a',\n",
       "    'src_path': 'dataset.csv',\n",
       "    'target_path': '/User/ml3/mlrun/examples/data/e9bdd9983067436eb080d9e61a458e4a/dataset.csv',\n",
       "    'hash': 'c64e1ae504eb958a7a507e5d6fe43645a1bfb034',\n",
       "    'viewer': 'table',\n",
       "    'size': 12,\n",
       "    'db_key': 'demo_dataset',\n",
       "    'header': ['A', 'B', 'C']},\n",
       "   {'key': 'chart',\n",
       "    'kind': 'chart',\n",
       "    'iter': 0,\n",
       "    'tree': 'e9bdd9983067436eb080d9e61a458e4a',\n",
       "    'target_path': '/User/ml3/mlrun/examples/data/e9bdd9983067436eb080d9e61a458e4a/chart.html',\n",
       "    'hash': '7affd9d4dc56ef2ffe9f5e7cfa4f010b64c24a6e',\n",
       "    'viewer': 'chart'},\n",
       "   {'key': 'mydf',\n",
       "    'kind': 'dataset',\n",
       "    'iter': 0,\n",
       "    'tree': 'e9bdd9983067436eb080d9e61a458e4a',\n",
       "    'target_path': '/User/ml3/mlrun/examples/data/e9bdd9983067436eb080d9e61a458e4a/mydf.csv',\n",
       "    'hash': 'c88f9b99f93955a66a40a8bba377d1022d931f63',\n",
       "    'format': 'csv',\n",
       "    'size': 135,\n",
       "    'db_key': 'demo_mydf',\n",
       "    'schema': {'fields': [{'name': 'index', 'type': 'integer'},\n",
       "      {'name': 'first_name', 'type': 'string'},\n",
       "      {'name': 'last_name', 'type': 'string'},\n",
       "      {'name': 'age', 'type': 'integer'},\n",
       "      {'name': 'testScore', 'type': 'integer'}],\n",
       "     'primaryKey': ['index'],\n",
       "     'pandas_version': '0.20.0'},\n",
       "    'header': ['index', 'first_name', 'last_name', 'age', 'testScore'],\n",
       "    'length': 5,\n",
       "    'preview': [[0, 'Jason', 'Miller', 42, 25],\n",
       "     [1, 'Molly', 'Jacobson', 52, 94],\n",
       "     [2, 'Tina', 'Ali', 36, 57],\n",
       "     [3, 'Jake', 'Milner', 24, 62],\n",
       "     [4, 'Amy', 'Cooze', 73, 70]],\n",
       "    'stats': {'first_name': {'count': 5.0,\n",
       "      'unique': 5.0,\n",
       "      'top': 'Molly',\n",
       "      'freq': 1.0},\n",
       "     'last_name': {'count': 5.0, 'unique': 5.0, 'top': 'Milner', 'freq': 1.0},\n",
       "     'age': {'count': 5.0,\n",
       "      'mean': 45.4,\n",
       "      'std': 18.46076921474292,\n",
       "      'min': 24.0,\n",
       "      '25%': 36.0,\n",
       "      '50%': 42.0,\n",
       "      '75%': 52.0,\n",
       "      'max': 73.0},\n",
       "     'testScore': {'count': 5.0,\n",
       "      'mean': 61.6,\n",
       "      'std': 24.90582261239327,\n",
       "      'min': 25.0,\n",
       "      '25%': 57.0,\n",
       "      '50%': 62.0,\n",
       "      '75%': 70.0,\n",
       "      'max': 94.0}}}],\n",
       "  'start_time': '2020-06-08T16:40:41.607283+00:00',\n",
       "  'last_update': '2020-06-08T16:40:41.607291+00:00'}}"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "run_object.to_dict()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'completed'"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "run_object.state()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style> \n",
       ".dictlist {\n",
       "  background-color: #b3edff; \n",
       "  text-align: center; \n",
       "  margin: 4px; \n",
       "  border-radius: 3px; padding: 0px 3px 1px 3px; display: inline-block;}\n",
       ".artifact {\n",
       "  cursor: pointer; \n",
       "  background-color: #ffe6cc; \n",
       "  text-align: left; \n",
       "  margin: 4px; border-radius: 3px; padding: 0px 3px 1px 3px; display: inline-block;\n",
       "}\n",
       "div.block.hidden {\n",
       "  display: none;\n",
       "}\n",
       ".clickable {\n",
       "  cursor: pointer;\n",
       "}\n",
       ".ellipsis {\n",
       "  display: inline-block;\n",
       "  max-width: 60px;\n",
       "  white-space: nowrap;\n",
       "  overflow: hidden;\n",
       "  text-overflow: ellipsis;\n",
       "}\n",
       ".master-wrapper {\n",
       "  display: flex;\n",
       "  flex-flow: row nowrap;\n",
       "  justify-content: flex-start;\n",
       "  align-items: stretch;\n",
       "}\n",
       ".master-tbl {\n",
       "  flex: 3\n",
       "}\n",
       ".master-wrapper > div {\n",
       "  margin: 4px;\n",
       "  padding: 10px;\n",
       "}\n",
       "iframe.fileview {\n",
       "  border: 0 none;\n",
       "  height: 100%;\n",
       "  width: 100%;\n",
       "  white-space: pre-wrap;\n",
       "}\n",
       ".pane-header-title {\n",
       "  width: 80%;\n",
       "  font-weight: 500;\n",
       "}\n",
       ".pane-header {\n",
       "  line-height: 1;\n",
       "  background-color: #ffe6cc;\n",
       "  padding: 3px;\n",
       "}\n",
       ".pane-header .close {\n",
       "  font-size: 20px;\n",
       "  font-weight: 700;\n",
       "  float: right;\n",
       "  margin-top: -5px;\n",
       "}\n",
       ".master-wrapper .right-pane {\n",
       "  border: 1px inset silver;\n",
       "  width: 40%;\n",
       "  min-height: 300px;\n",
       "  flex: 3\n",
       "  min-width: 500px;\n",
       "}\n",
       ".master-wrapper * {\n",
       "  box-sizing: border-box;\n",
       "}\n",
       "</style><script>\n",
       "function copyToClipboard(fld) {\n",
       "    if (document.queryCommandSupported && document.queryCommandSupported('copy')) {\n",
       "        var textarea = document.createElement('textarea');\n",
       "        textarea.textContent = fld.innerHTML;\n",
       "        textarea.style.position = 'fixed';\n",
       "        document.body.appendChild(textarea);\n",
       "        textarea.select();\n",
       "\n",
       "        try {\n",
       "            return document.execCommand('copy'); // Security exception may be thrown by some browsers.\n",
       "        } catch (ex) {\n",
       "\n",
       "        } finally {\n",
       "            document.body.removeChild(textarea);\n",
       "        }\n",
       "    }\n",
       "}\n",
       "function expandPanel(el) {\n",
       "  const panelName = \"#\" + el.getAttribute('paneName');\n",
       "  console.log(el.title);\n",
       "\n",
       "  document.querySelector(panelName + \"-title\").innerHTML = el.title\n",
       "  iframe = document.querySelector(panelName + \"-body\");\n",
       "  \n",
       "  const tblcss = `<style> body { font-family: Arial, Helvetica, sans-serif;}\n",
       "    #csv { margin-bottom: 15px; }\n",
       "    #csv table { border-collapse: collapse;}\n",
       "    #csv table td { padding: 4px 8px; border: 1px solid silver;} </style>`;\n",
       "\n",
       "  function csvToHtmlTable(str) {\n",
       "    return '<div id=\"csv\"><table><tr><td>' +  str.replace(/[\\n\\r]+$/g, '').replace(/[\\n\\r]+/g, '</td></tr><tr><td>')\n",
       "      .replace(/,/g, '</td><td>') + '</td></tr></table></div>';\n",
       "  }\n",
       "  \n",
       "  function reqListener () {\n",
       "    if (el.title.endsWith(\".csv\")) {\n",
       "      iframe.setAttribute(\"srcdoc\", tblcss + csvToHtmlTable(this.responseText));\n",
       "    } else {\n",
       "      iframe.setAttribute(\"srcdoc\", this.responseText);\n",
       "    }  \n",
       "    console.log(this.responseText);\n",
       "  }\n",
       "\n",
       "  const oReq = new XMLHttpRequest();\n",
       "  oReq.addEventListener(\"load\", reqListener);\n",
       "  oReq.open(\"GET\", el.title);\n",
       "  oReq.send();\n",
       "  \n",
       "  \n",
       "  //iframe.src = el.title;\n",
       "  const resultPane = document.querySelector(panelName + \"-pane\");\n",
       "  if (resultPane.classList.contains(\"hidden\")) {\n",
       "    resultPane.classList.remove(\"hidden\");\n",
       "  }\n",
       "}\n",
       "function closePanel(el) {\n",
       "  const panelName = \"#\" + el.getAttribute('paneName')\n",
       "  const resultPane = document.querySelector(panelName + \"-pane\");\n",
       "  if (!resultPane.classList.contains(\"hidden\")) {\n",
       "    resultPane.classList.add(\"hidden\");\n",
       "  }\n",
       "}\n",
       "\n",
       "</script>\n",
       "<div class=\"master-wrapper\">\n",
       "  <div class=\"block master-tbl\"><div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>project</th>\n",
       "      <th>uid</th>\n",
       "      <th>iter</th>\n",
       "      <th>start</th>\n",
       "      <th>state</th>\n",
       "      <th>name</th>\n",
       "      <th>labels</th>\n",
       "      <th>inputs</th>\n",
       "      <th>parameters</th>\n",
       "      <th>results</th>\n",
       "      <th>artifacts</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>default</td>\n",
       "      <td><div title=\"e9bdd9983067436eb080d9e61a458e4a\"><a href=\"https://mlrun-ui.default-tenant.app.yh55.iguazio-cd2.com/projects/default/jobs/e9bdd9983067436eb080d9e61a458e4a/info\" target=\"_blank\" >...1a458e4a</a></div></td>\n",
       "      <td>0</td>\n",
       "      <td>Jun 08 16:40:41</td>\n",
       "      <td>completed</td>\n",
       "      <td>demo</td>\n",
       "      <td><div class=\"dictlist\">type=demo</div><div class=\"dictlist\">v3io_user=admin</div><div class=\"dictlist\">kind=</div><div class=\"dictlist\">owner=admin</div><div class=\"dictlist\">host=jupyter-65887d7ffb-5jsn2</div><div class=\"dictlist\">framework=sklearn</div></td>\n",
       "      <td><div class=\"artifact\" onclick=\"expandPanel(this)\" paneName=\"result3ba4dccd\" title=\"/files/ml3/mlrun/examples/infile.txt\">infile.txt</div></td>\n",
       "      <td><div class=\"dictlist\">p1=5</div><div class=\"dictlist\">p2=a-string</div></td>\n",
       "      <td><div class=\"dictlist\">accuracy=10</div><div class=\"dictlist\">loss=15</div></td>\n",
       "      <td><div class=\"artifact\" onclick=\"expandPanel(this)\" paneName=\"result3ba4dccd\" title=\"/files/ml3/mlrun/examples/data/e9bdd9983067436eb080d9e61a458e4a/model.txt\">model</div><div class=\"artifact\" onclick=\"expandPanel(this)\" paneName=\"result3ba4dccd\" title=\"/files/ml3/mlrun/examples/data/e9bdd9983067436eb080d9e61a458e4a/result.html\">html_result</div><div class=\"artifact\" onclick=\"expandPanel(this)\" paneName=\"result3ba4dccd\" title=\"/files/ml3/mlrun/examples/data/e9bdd9983067436eb080d9e61a458e4a/dataset.csv\">dataset</div><div class=\"artifact\" onclick=\"expandPanel(this)\" paneName=\"result3ba4dccd\" title=\"/files/ml3/mlrun/examples/data/e9bdd9983067436eb080d9e61a458e4a/chart.html\">chart</div><div class=\"artifact\" onclick=\"expandPanel(this)\" paneName=\"result3ba4dccd\" title=\"/files/ml3/mlrun/examples/data/e9bdd9983067436eb080d9e61a458e4a/mydf.csv\">mydf</div></td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div></div>\n",
       "  <div id=\"result3ba4dccd-pane\" class=\"right-pane block hidden\">\n",
       "    <div class=\"pane-header\">\n",
       "      <span id=\"result3ba4dccd-title\" class=\"pane-header-title\">Title</span>\n",
       "      <span onclick=\"closePanel(this)\" paneName=\"result3ba4dccd\" class=\"close clickable\">&times;</span>\n",
       "    </div>\n",
       "    <iframe class=\"fileview\" id=\"result3ba4dccd-body\"></iframe>\n",
       "  </div>\n",
       "</div>\n"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "run_object.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'accuracy': 10,\n",
       " 'loss': 15,\n",
       " 'model': '/User/ml3/mlrun/examples/data/e9bdd9983067436eb080d9e61a458e4a/model.txt',\n",
       " 'html_result': '/User/ml3/mlrun/examples/data/e9bdd9983067436eb080d9e61a458e4a/result.html',\n",
       " 'dataset': '/User/ml3/mlrun/examples/data/e9bdd9983067436eb080d9e61a458e4a/dataset.csv',\n",
       " 'chart': '/User/ml3/mlrun/examples/data/e9bdd9983067436eb080d9e61a458e4a/chart.html',\n",
       " 'mydf': 'store://default/demo_mydf#e9bdd9983067436eb080d9e61a458e4a'}"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "run_object.outputs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[mlrun] 2020-06-08 16:40:41,607 logging run results to: http://mlrun-api:8080\n",
      "Run: demo (uid=e9bdd9983067436eb080d9e61a458e4a)\n",
      "Params: p1=5, p2=a-string\n",
      "accesskey = 456\n",
      "file\n",
      "b\"I'm a local input file\\n\"\n",
      "\n",
      "[mlrun] 2020-06-08 16:40:41,679 log artifact model at /User/ml3/mlrun/examples/data/e9bdd9983067436eb080d9e61a458e4a/model.txt, size: 10, db: Y\n",
      "[mlrun] 2020-06-08 16:40:41,702 log artifact html_result at /User/ml3/mlrun/examples/data/e9bdd9983067436eb080d9e61a458e4a/result.html, size: 17, db: Y\n",
      "[mlrun] 2020-06-08 16:40:41,725 log artifact dataset at /User/ml3/mlrun/examples/data/e9bdd9983067436eb080d9e61a458e4a/dataset.csv, size: 12, db: Y\n",
      "[mlrun] 2020-06-08 16:40:41,748 log artifact chart at /User/ml3/mlrun/examples/data/e9bdd9983067436eb080d9e61a458e4a/chart.html, size: 881, db: Y\n",
      "[mlrun] 2020-06-08 16:40:41,807 log artifact mydf at /User/ml3/mlrun/examples/data/e9bdd9983067436eb080d9e61a458e4a/mydf.csv, size: 135, db: Y\n",
      "\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "''"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "run_object.logs()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'key': 'dataset',\n",
       " 'kind': 'table',\n",
       " 'iter': 0,\n",
       " 'tree': 'e9bdd9983067436eb080d9e61a458e4a',\n",
       " 'src_path': 'dataset.csv',\n",
       " 'target_path': '/User/ml3/mlrun/examples/data/e9bdd9983067436eb080d9e61a458e4a/dataset.csv',\n",
       " 'hash': 'c64e1ae504eb958a7a507e5d6fe43645a1bfb034',\n",
       " 'viewer': 'table',\n",
       " 'size': 12,\n",
       " 'db_key': 'demo_dataset',\n",
       " 'header': ['A', 'B', 'C']}"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "run_object.artifact(\"dataset\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id=\"using-hyperparamter-tasks\"></a>\n",
    "______________________________________________"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# **using hyper-parameter tasks**\n",
    "In many cases we want to run the same function with different input values and select the best result.<br>\n",
    "\n",
    "You can specify parameters with a list of values and mlrun will run all the parameter combinations as a single hyper-param task.<br>\n",
    "\n",
    "Each unique run combination is called an _**iteration**_, where '0' iteration is the parent task.\n",
    "\n",
    "Use `.with_hyper_params()` and provide lists or values, we use the selector string to indicate which will iteration will be selected as the winning result (indicated using [```min``` or ```max```].[```output-value```])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[mlrun] 2020-06-08 16:43:26,876 starting run demo uid=9f19c02aa8e4438bbf1ad5fe1f88740a  -> http://mlrun-api:8080\n",
      "[mlrun] 2020-06-08 16:43:26,906 starting local run: training.py # main\n",
      "> --------------- Iteration: (1) ---------------\n",
      "[mlrun] 2020-06-08 16:43:29,489 logging run results to: http://mlrun-api:8080\n",
      "Run: demo (uid=9f19c02aa8e4438bbf1ad5fe1f88740a-1)\n",
      "Params: p1=5, p2=5\n",
      "accesskey = 456\n",
      "file\n",
      "b\"I'm a local input file\\n\"\n",
      "\n",
      "[mlrun] 2020-06-08 16:43:29,567 log artifact model at /User/ml3/mlrun/examples/data/9f19c02aa8e4438bbf1ad5fe1f88740a/1/model.txt, size: 10, db: Y\n",
      "[mlrun] 2020-06-08 16:43:29,597 log artifact html_result at /User/ml3/mlrun/examples/data/9f19c02aa8e4438bbf1ad5fe1f88740a/1/result.html, size: 17, db: Y\n",
      "[mlrun] 2020-06-08 16:43:29,622 log artifact dataset at /User/ml3/mlrun/examples/data/9f19c02aa8e4438bbf1ad5fe1f88740a/1/dataset.csv, size: 12, db: Y\n",
      "[mlrun] 2020-06-08 16:43:29,646 log artifact chart at /User/ml3/mlrun/examples/data/9f19c02aa8e4438bbf1ad5fe1f88740a/1/chart.html, size: 881, db: Y\n",
      "[mlrun] 2020-06-08 16:43:29,702 log artifact mydf at /User/ml3/mlrun/examples/data/9f19c02aa8e4438bbf1ad5fe1f88740a/1/mydf.csv, size: 135, db: Y\n",
      "\n",
      "[mlrun] 2020-06-08 16:43:30,057 starting local run: training.py # main\n",
      "> --------------- Iteration: (2) ---------------\n",
      "[mlrun] 2020-06-08 16:43:32,569 logging run results to: http://mlrun-api:8080\n",
      "Run: demo (uid=9f19c02aa8e4438bbf1ad5fe1f88740a-2)\n",
      "Params: p1=5, p2=2\n",
      "accesskey = 456\n",
      "file\n",
      "b\"I'm a local input file\\n\"\n",
      "\n",
      "[mlrun] 2020-06-08 16:43:32,634 log artifact model at /User/ml3/mlrun/examples/data/9f19c02aa8e4438bbf1ad5fe1f88740a/2/model.txt, size: 10, db: Y\n",
      "[mlrun] 2020-06-08 16:43:32,658 log artifact html_result at /User/ml3/mlrun/examples/data/9f19c02aa8e4438bbf1ad5fe1f88740a/2/result.html, size: 17, db: Y\n",
      "[mlrun] 2020-06-08 16:43:32,680 log artifact dataset at /User/ml3/mlrun/examples/data/9f19c02aa8e4438bbf1ad5fe1f88740a/2/dataset.csv, size: 12, db: Y\n",
      "[mlrun] 2020-06-08 16:43:32,703 log artifact chart at /User/ml3/mlrun/examples/data/9f19c02aa8e4438bbf1ad5fe1f88740a/2/chart.html, size: 881, db: Y\n",
      "[mlrun] 2020-06-08 16:43:32,761 log artifact mydf at /User/ml3/mlrun/examples/data/9f19c02aa8e4438bbf1ad5fe1f88740a/2/mydf.csv, size: 135, db: Y\n",
      "\n",
      "[mlrun] 2020-06-08 16:43:33,118 starting local run: training.py # main\n",
      "> --------------- Iteration: (3) ---------------\n",
      "[mlrun] 2020-06-08 16:43:35,626 logging run results to: http://mlrun-api:8080\n",
      "Run: demo (uid=9f19c02aa8e4438bbf1ad5fe1f88740a-3)\n",
      "Params: p1=5, p2=3\n",
      "accesskey = 456\n",
      "file\n",
      "b\"I'm a local input file\\n\"\n",
      "\n",
      "[mlrun] 2020-06-08 16:43:35,688 log artifact model at /User/ml3/mlrun/examples/data/9f19c02aa8e4438bbf1ad5fe1f88740a/3/model.txt, size: 10, db: Y\n",
      "[mlrun] 2020-06-08 16:43:35,715 log artifact html_result at /User/ml3/mlrun/examples/data/9f19c02aa8e4438bbf1ad5fe1f88740a/3/result.html, size: 17, db: Y\n",
      "[mlrun] 2020-06-08 16:43:35,748 log artifact dataset at /User/ml3/mlrun/examples/data/9f19c02aa8e4438bbf1ad5fe1f88740a/3/dataset.csv, size: 12, db: Y\n",
      "[mlrun] 2020-06-08 16:43:35,775 log artifact chart at /User/ml3/mlrun/examples/data/9f19c02aa8e4438bbf1ad5fe1f88740a/3/chart.html, size: 881, db: Y\n",
      "[mlrun] 2020-06-08 16:43:35,835 log artifact mydf at /User/ml3/mlrun/examples/data/9f19c02aa8e4438bbf1ad5fe1f88740a/3/mydf.csv, size: 135, db: Y\n",
      "\n",
      "[mlrun] 2020-06-08 16:43:36,193 best iteration=1, used criteria min.loss\n",
      "[mlrun] 2020-06-08 16:43:36,264 log artifact iteration_results at /User/ml3/mlrun/examples/data/9f19c02aa8e4438bbf1ad5fe1f88740a/iteration_results.csv, size: 123, db: Y\n"
     ]
    },
    {
     "data": {
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       "<style> \n",
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       "}\n",
       "</style><script>\n",
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       "        textarea.select();\n",
       "\n",
       "        try {\n",
       "            return document.execCommand('copy'); // Security exception may be thrown by some browsers.\n",
       "        } catch (ex) {\n",
       "\n",
       "        } finally {\n",
       "            document.body.removeChild(textarea);\n",
       "        }\n",
       "    }\n",
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       "  const panelName = \"#\" + el.getAttribute('paneName');\n",
       "  console.log(el.title);\n",
       "\n",
       "  document.querySelector(panelName + \"-title\").innerHTML = el.title\n",
       "  iframe = document.querySelector(panelName + \"-body\");\n",
       "  \n",
       "  const tblcss = `<style> body { font-family: Arial, Helvetica, sans-serif;}\n",
       "    #csv { margin-bottom: 15px; }\n",
       "    #csv table { border-collapse: collapse;}\n",
       "    #csv table td { padding: 4px 8px; border: 1px solid silver;} </style>`;\n",
       "\n",
       "  function csvToHtmlTable(str) {\n",
       "    return '<div id=\"csv\"><table><tr><td>' +  str.replace(/[\\n\\r]+$/g, '').replace(/[\\n\\r]+/g, '</td></tr><tr><td>')\n",
       "      .replace(/,/g, '</td><td>') + '</td></tr></table></div>';\n",
       "  }\n",
       "  \n",
       "  function reqListener () {\n",
       "    if (el.title.endsWith(\".csv\")) {\n",
       "      iframe.setAttribute(\"srcdoc\", tblcss + csvToHtmlTable(this.responseText));\n",
       "    } else {\n",
       "      iframe.setAttribute(\"srcdoc\", this.responseText);\n",
       "    }  \n",
       "    console.log(this.responseText);\n",
       "  }\n",
       "\n",
       "  const oReq = new XMLHttpRequest();\n",
       "  oReq.addEventListener(\"load\", reqListener);\n",
       "  oReq.open(\"GET\", el.title);\n",
       "  oReq.send();\n",
       "  \n",
       "  \n",
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       "  if (resultPane.classList.contains(\"hidden\")) {\n",
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       "  if (!resultPane.classList.contains(\"hidden\")) {\n",
       "    resultPane.classList.add(\"hidden\");\n",
       "  }\n",
       "}\n",
       "\n",
       "</script>\n",
       "<div class=\"master-wrapper\">\n",
       "  <div class=\"block master-tbl\"><div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>project</th>\n",
       "      <th>uid</th>\n",
       "      <th>iter</th>\n",
       "      <th>start</th>\n",
       "      <th>state</th>\n",
       "      <th>name</th>\n",
       "      <th>labels</th>\n",
       "      <th>inputs</th>\n",
       "      <th>parameters</th>\n",
       "      <th>results</th>\n",
       "      <th>artifacts</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>default</td>\n",
       "      <td><div title=\"9f19c02aa8e4438bbf1ad5fe1f88740a\"><a href=\"https://mlrun-ui.default-tenant.app.yh55.iguazio-cd2.com/projects/default/jobs/9f19c02aa8e4438bbf1ad5fe1f88740a/info\" target=\"_blank\" >...1f88740a</a></div></td>\n",
       "      <td>0</td>\n",
       "      <td>Jun 08 16:43:26</td>\n",
       "      <td>completed</td>\n",
       "      <td>demo</td>\n",
       "      <td><div class=\"dictlist\">type=demo</div><div class=\"dictlist\">v3io_user=admin</div><div class=\"dictlist\">kind=</div><div class=\"dictlist\">owner=admin</div></td>\n",
       "      <td></td>\n",
       "      <td><div class=\"dictlist\">p1=5</div></td>\n",
       "      <td><div class=\"dictlist\">best_iteration=1</div><div class=\"dictlist\">accuracy=10</div><div class=\"dictlist\">loss=15</div></td>\n",
       "      <td><div class=\"artifact\" onclick=\"expandPanel(this)\" paneName=\"resultaf45e029\" title=\"/files/ml3/mlrun/examples/data/9f19c02aa8e4438bbf1ad5fe1f88740a/1/model.txt\">model</div><div class=\"artifact\" onclick=\"expandPanel(this)\" paneName=\"resultaf45e029\" title=\"/files/ml3/mlrun/examples/data/9f19c02aa8e4438bbf1ad5fe1f88740a/1/result.html\">html_result</div><div class=\"artifact\" onclick=\"expandPanel(this)\" paneName=\"resultaf45e029\" title=\"/files/ml3/mlrun/examples/data/9f19c02aa8e4438bbf1ad5fe1f88740a/1/dataset.csv\">dataset</div><div class=\"artifact\" onclick=\"expandPanel(this)\" paneName=\"resultaf45e029\" title=\"/files/ml3/mlrun/examples/data/9f19c02aa8e4438bbf1ad5fe1f88740a/1/chart.html\">chart</div><div class=\"artifact\" onclick=\"expandPanel(this)\" paneName=\"resultaf45e029\" title=\"/files/ml3/mlrun/examples/data/9f19c02aa8e4438bbf1ad5fe1f88740a/1/mydf.csv\">mydf</div><div class=\"artifact\" onclick=\"expandPanel(this)\" paneName=\"resultaf45e029\" title=\"/files/ml3/mlrun/examples/data/9f19c02aa8e4438bbf1ad5fe1f88740a/iteration_results.csv\">iteration_results</div></td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div></div>\n",
       "  <div id=\"resultaf45e029-pane\" class=\"right-pane block hidden\">\n",
       "    <div class=\"pane-header\">\n",
       "      <span id=\"resultaf45e029-title\" class=\"pane-header-title\">Title</span>\n",
       "      <span onclick=\"closePanel(this)\" paneName=\"resultaf45e029\" class=\"close clickable\">&times;</span>\n",
       "    </div>\n",
       "    <iframe class=\"fileview\" id=\"resultaf45e029-body\"></iframe>\n",
       "  </div>\n",
       "</div>\n"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "to track results use .show() or .logs() or in CLI: \n",
      "!mlrun get run 9f19c02aa8e4438bbf1ad5fe1f88740a --project default , !mlrun logs 9f19c02aa8e4438bbf1ad5fe1f88740a --project default\n",
      "[mlrun] 2020-06-08 16:43:36,298 run executed, status=completed\n"
     ]
    }
   ],
   "source": [
    "run = run_local(\n",
    "    task.with_hyper_params({\"p2\": [5, 2, 3]}, \"min.loss\"), command=\"training.py\"\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'best_iteration': 1,\n",
       " 'accuracy': 10,\n",
       " 'loss': 15,\n",
       " 'model': '/User/ml3/mlrun/examples/data/9f19c02aa8e4438bbf1ad5fe1f88740a/1/model.txt',\n",
       " 'html_result': '/User/ml3/mlrun/examples/data/9f19c02aa8e4438bbf1ad5fe1f88740a/1/result.html',\n",
       " 'dataset': '/User/ml3/mlrun/examples/data/9f19c02aa8e4438bbf1ad5fe1f88740a/1/dataset.csv',\n",
       " 'chart': '/User/ml3/mlrun/examples/data/9f19c02aa8e4438bbf1ad5fe1f88740a/1/chart.html',\n",
       " 'mydf': 'store://default/demo_mydf#9f19c02aa8e4438bbf1ad5fe1f88740a',\n",
       " 'iteration_results': '/User/ml3/mlrun/examples/data/9f19c02aa8e4438bbf1ad5fe1f88740a/iteration_results.csv'}"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "run.outputs"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id=\"tasks-cli\"></a>\n",
    "______________________________________________"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# **running tasks through the cli**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "env: MLRUN_DBPATH=http://mlrun-api:8080\n"
     ]
    }
   ],
   "source": [
    "%env MLRUN_DBPATH={mlconf.dbpath}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[mlrun] 2020-06-08 16:44:03,966 starting run train_hyper uid=3d2ea381861346cba591fe9165e7279d  -> http://mlrun-api:8080\n",
      "[mlrun] 2020-06-08 16:44:03,993 starting local run: training.py # main\n",
      "> --------------- Iteration: (1) ---------------\n",
      "[mlrun] 2020-06-08 16:44:06,481 logging run results to: http://mlrun-api:8080\n",
      "Run: train_hyper (uid=3d2ea381861346cba591fe9165e7279d-1)\n",
      "Params: p1=3, p2=5\n",
      "accesskey = None\n",
      "file\n",
      "b\"I'm a local input file\\n\"\n",
      "\n",
      "[mlrun] 2020-06-08 16:44:06,544 log artifact model at /User/ml3/mlrun/examples/data/3d2ea381861346cba591fe9165e7279d/1/model.txt, size: 10, db: Y\n",
      "[mlrun] 2020-06-08 16:44:06,574 log artifact html_result at /User/ml3/mlrun/examples/data/3d2ea381861346cba591fe9165e7279d/1/result.html, size: 17, db: Y\n",
      "[mlrun] 2020-06-08 16:44:06,601 log artifact dataset at /User/ml3/mlrun/examples/data/3d2ea381861346cba591fe9165e7279d/1/dataset.csv, size: 12, db: Y\n",
      "[mlrun] 2020-06-08 16:44:06,625 log artifact chart at /User/ml3/mlrun/examples/data/3d2ea381861346cba591fe9165e7279d/1/chart.html, size: 881, db: Y\n",
      "[mlrun] 2020-06-08 16:44:06,687 log artifact mydf at /User/ml3/mlrun/examples/data/3d2ea381861346cba591fe9165e7279d/1/mydf.csv, size: 135, db: Y\n",
      "\n",
      "[mlrun] 2020-06-08 16:44:07,038 starting local run: training.py # main\n",
      "> --------------- Iteration: (2) ---------------\n",
      "[mlrun] 2020-06-08 16:44:09,564 logging run results to: http://mlrun-api:8080\n",
      "Run: train_hyper (uid=3d2ea381861346cba591fe9165e7279d-2)\n",
      "Params: p1=7, p2=5\n",
      "accesskey = None\n",
      "file\n",
      "b\"I'm a local input file\\n\"\n",
      "\n",
      "[mlrun] 2020-06-08 16:44:09,628 log artifact model at /User/ml3/mlrun/examples/data/3d2ea381861346cba591fe9165e7279d/2/model.txt, size: 10, db: Y\n",
      "[mlrun] 2020-06-08 16:44:09,651 log artifact html_result at /User/ml3/mlrun/examples/data/3d2ea381861346cba591fe9165e7279d/2/result.html, size: 17, db: Y\n",
      "[mlrun] 2020-06-08 16:44:09,675 log artifact dataset at /User/ml3/mlrun/examples/data/3d2ea381861346cba591fe9165e7279d/2/dataset.csv, size: 12, db: Y\n",
      "[mlrun] 2020-06-08 16:44:09,697 log artifact chart at /User/ml3/mlrun/examples/data/3d2ea381861346cba591fe9165e7279d/2/chart.html, size: 881, db: Y\n",
      "[mlrun] 2020-06-08 16:44:09,755 log artifact mydf at /User/ml3/mlrun/examples/data/3d2ea381861346cba591fe9165e7279d/2/mydf.csv, size: 135, db: Y\n",
      "\n",
      "[mlrun] 2020-06-08 16:44:10,204 starting local run: training.py # main\n",
      "> --------------- Iteration: (3) ---------------\n",
      "[mlrun] 2020-06-08 16:44:12,658 logging run results to: http://mlrun-api:8080\n",
      "Run: train_hyper (uid=3d2ea381861346cba591fe9165e7279d-3)\n",
      "Params: p1=5, p2=5\n",
      "accesskey = None\n",
      "file\n",
      "b\"I'm a local input file\\n\"\n",
      "\n",
      "[mlrun] 2020-06-08 16:44:12,714 log artifact model at /User/ml3/mlrun/examples/data/3d2ea381861346cba591fe9165e7279d/3/model.txt, size: 10, db: Y\n",
      "[mlrun] 2020-06-08 16:44:12,737 log artifact html_result at /User/ml3/mlrun/examples/data/3d2ea381861346cba591fe9165e7279d/3/result.html, size: 17, db: Y\n",
      "[mlrun] 2020-06-08 16:44:12,761 log artifact dataset at /User/ml3/mlrun/examples/data/3d2ea381861346cba591fe9165e7279d/3/dataset.csv, size: 12, db: Y\n",
      "[mlrun] 2020-06-08 16:44:12,786 log artifact chart at /User/ml3/mlrun/examples/data/3d2ea381861346cba591fe9165e7279d/3/chart.html, size: 881, db: Y\n",
      "[mlrun] 2020-06-08 16:44:12,845 log artifact mydf at /User/ml3/mlrun/examples/data/3d2ea381861346cba591fe9165e7279d/3/mydf.csv, size: 135, db: Y\n",
      "\n",
      "[mlrun] 2020-06-08 16:44:13,207 best iteration=2, used criteria max.accuracy\n",
      "[mlrun] 2020-06-08 16:44:13,286 log artifact iteration_results at /User/ml3/mlrun/examples/data/3d2ea381861346cba591fe9165e7279d/iteration_results.csv, size: 121, db: Y\n",
      "[mlrun] 2020-06-08 16:44:13,305 run executed, status=completed\n"
     ]
    }
   ],
   "source": [
    "!mlrun run --name train_hyper -x p1=\"[3,7,5]\" --selector max.accuracy -p p2=5 --out-path {artifact_path} training.py\n",
    "if _exit_code != 0:\n",
    "    raise RuntimeError()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Usage: mlrun [OPTIONS] COMMAND [ARGS]...\n",
      "\n",
      "Options:\n",
      "  --help  Show this message and exit.\n",
      "\n",
      "Commands:\n",
      "  build    Build a container image from code and requirements.\n",
      "  clean    Clean runtime resources\n",
      "  config   Show configuration & exit\n",
      "  db       Run HTTP api/database server\n",
      "  deploy   Deploy model or function\n",
      "  get      List/get one or more object per kind/class.\n",
      "  logs     Get or watch task logs\n",
      "  project  load and/or run a project\n",
      "  run      Execute a task and inject parameters.\n",
      "  version  get mlrun version\n",
      "  watch    Read current or previous task (pod) logs.\n"
     ]
    }
   ],
   "source": [
    "# see other CLI commands\n",
    "!mlrun\n",
    "if _exit_code != 0:\n",
    "    raise RuntimeError()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id=\"inline\"></a>\n",
    "______________________________________________"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# **using (inline) code and running on different runtimes**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "from mlrun.artifacts import ChartArtifact, PlotArtifact\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "\n",
    "# define a function with spec as parameter\n",
    "import time\n",
    "\n",
    "\n",
    "def handler(context, p1=1, p2=\"xx\"):\n",
    "    \"\"\"this is a simple function\n",
    "\n",
    "    :param p1:  first param\n",
    "    :param p2:  another param\n",
    "    \"\"\"\n",
    "    # access input metadata, values, and inputs\n",
    "    print(f\"Run: {context.name} (uid={context.uid})\")\n",
    "    print(f\"Params: p1={p1}, p2={p2}\")\n",
    "\n",
    "    time.sleep(1)\n",
    "\n",
    "    # log the run results (scalar values)\n",
    "    context.log_result(\"accuracy\", p1 * 2)\n",
    "    context.log_result(\"loss\", p1 * 3)\n",
    "\n",
    "    # add a lable/tag to this run\n",
    "    context.set_label(\"category\", \"tests\")\n",
    "\n",
    "    # create a matplot figure and store as artifact\n",
    "    fig, ax = plt.subplots()\n",
    "    np.random.seed(0)\n",
    "    x, y = np.random.normal(size=(2, 200))\n",
    "    color, size = np.random.random((2, 200))\n",
    "    ax.scatter(x, y, c=color, s=500 * size, alpha=0.3)\n",
    "    ax.grid(color=\"lightgray\", alpha=0.7)\n",
    "\n",
    "    context.log_artifact(PlotArtifact(\"myfig\", body=fig, title=\"my plot\"))\n",
    "\n",
    "    # create a dataframe artifact\n",
    "    df = pd.DataFrame([{\"A\": 10, \"B\": 100}, {\"A\": 11, \"B\": 110}, {\"A\": 12, \"B\": 120}])\n",
    "    context.log_dataset(\"mydf\", df=df)\n",
    "\n",
    "    # Log an ML Model artifact\n",
    "    context.log_model(\n",
    "        \"mymodel\",\n",
    "        body=b\"abc is 123\",\n",
    "        model_file=\"model.txt\",\n",
    "        model_dir=\"data\",\n",
    "        metrics={\"accuracy\": 0.85},\n",
    "        parameters={\"xx\": \"abc\"},\n",
    "        labels={\"framework\": \"xgboost\"},\n",
    "    )\n",
    "\n",
    "    return \"my resp\""
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id=\"run-locally\"></a>\n",
    "______________________________________________"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# **run locally in the notebook**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[mlrun] 2020-06-08 16:45:44,526 starting run demo2 uid=cfeecfa9880b4047b08685a0e62bddaa  -> http://mlrun-api:8080\n",
      "Run: demo2 (uid=cfeecfa9880b4047b08685a0e62bddaa)\n",
      "Params: p1=7, p2=xx\n",
      "[mlrun] 2020-06-08 16:45:45,641 log artifact myfig at /User/ml3/mlrun/examples/data/cfeecfa9880b4047b08685a0e62bddaa/myfig.html, size: 76607, db: Y\n",
      "[mlrun] 2020-06-08 16:45:45,671 log artifact mydf at /User/ml3/mlrun/examples/data/cfeecfa9880b4047b08685a0e62bddaa/mydf.csv, size: 32, db: Y\n",
      "[mlrun] 2020-06-08 16:45:45,690 log artifact mymodel at /User/ml3/mlrun/examples/data/cfeecfa9880b4047b08685a0e62bddaa/data/, size: 10, db: Y\n",
      "\n"
     ]
    },
    {
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       "  border-radius: 3px; padding: 0px 3px 1px 3px; display: inline-block;}\n",
       ".artifact {\n",
       "  cursor: pointer; \n",
       "  background-color: #ffe6cc; \n",
       "  text-align: left; \n",
       "  margin: 4px; border-radius: 3px; padding: 0px 3px 1px 3px; display: inline-block;\n",
       "}\n",
       "div.block.hidden {\n",
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       ".clickable {\n",
       "  cursor: pointer;\n",
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       "  display: flex;\n",
       "  flex-flow: row nowrap;\n",
       "  justify-content: flex-start;\n",
       "  align-items: stretch;\n",
       "}\n",
       ".master-tbl {\n",
       "  flex: 3\n",
       "}\n",
       ".master-wrapper > div {\n",
       "  margin: 4px;\n",
       "  padding: 10px;\n",
       "}\n",
       "iframe.fileview {\n",
       "  border: 0 none;\n",
       "  height: 100%;\n",
       "  width: 100%;\n",
       "  white-space: pre-wrap;\n",
       "}\n",
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       "  width: 80%;\n",
       "  font-weight: 500;\n",
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       ".master-wrapper .right-pane {\n",
       "  border: 1px inset silver;\n",
       "  width: 40%;\n",
       "  min-height: 300px;\n",
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       "\n",
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       "\n",
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       "\n",
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       "  iframe = document.querySelector(panelName + \"-body\");\n",
       "  \n",
       "  const tblcss = `<style> body { font-family: Arial, Helvetica, sans-serif;}\n",
       "    #csv { margin-bottom: 15px; }\n",
       "    #csv table { border-collapse: collapse;}\n",
       "    #csv table td { padding: 4px 8px; border: 1px solid silver;} </style>`;\n",
       "\n",
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       "      .replace(/,/g, '</td><td>') + '</td></tr></table></div>';\n",
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       "  \n",
       "  function reqListener () {\n",
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       "      iframe.setAttribute(\"srcdoc\", tblcss + csvToHtmlTable(this.responseText));\n",
       "    } else {\n",
       "      iframe.setAttribute(\"srcdoc\", this.responseText);\n",
       "    }  \n",
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       "  }\n",
       "\n",
       "  const oReq = new XMLHttpRequest();\n",
       "  oReq.addEventListener(\"load\", reqListener);\n",
       "  oReq.open(\"GET\", el.title);\n",
       "  oReq.send();\n",
       "  \n",
       "  \n",
       "  //iframe.src = el.title;\n",
       "  const resultPane = document.querySelector(panelName + \"-pane\");\n",
       "  if (resultPane.classList.contains(\"hidden\")) {\n",
       "    resultPane.classList.remove(\"hidden\");\n",
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       "  const panelName = \"#\" + el.getAttribute('paneName')\n",
       "  const resultPane = document.querySelector(panelName + \"-pane\");\n",
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       "    resultPane.classList.add(\"hidden\");\n",
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       "}\n",
       "\n",
       "</script>\n",
       "<div class=\"master-wrapper\">\n",
       "  <div class=\"block master-tbl\"><div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>project</th>\n",
       "      <th>uid</th>\n",
       "      <th>iter</th>\n",
       "      <th>start</th>\n",
       "      <th>state</th>\n",
       "      <th>name</th>\n",
       "      <th>labels</th>\n",
       "      <th>inputs</th>\n",
       "      <th>parameters</th>\n",
       "      <th>results</th>\n",
       "      <th>artifacts</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>default</td>\n",
       "      <td><div title=\"cfeecfa9880b4047b08685a0e62bddaa\"><a href=\"https://mlrun-ui.default-tenant.app.yh55.iguazio-cd2.com/projects/default/jobs/cfeecfa9880b4047b08685a0e62bddaa/info\" target=\"_blank\" >...e62bddaa</a></div></td>\n",
       "      <td>0</td>\n",
       "      <td>Jun 08 16:45:44</td>\n",
       "      <td>completed</td>\n",
       "      <td>demo2</td>\n",
       "      <td><div class=\"dictlist\">v3io_user=admin</div><div class=\"dictlist\">kind=handler</div><div class=\"dictlist\">owner=admin</div><div class=\"dictlist\">host=jupyter-65887d7ffb-5jsn2</div><div class=\"dictlist\">category=tests</div></td>\n",
       "      <td></td>\n",
       "      <td><div class=\"dictlist\">p1=7</div></td>\n",
       "      <td><div class=\"dictlist\">accuracy=14</div><div class=\"dictlist\">loss=21</div><div class=\"dictlist\">return=my resp</div></td>\n",
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       "</div></div>\n",
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       "    <div class=\"pane-header\">\n",
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       "  </div>\n",
       "</div>\n"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "to track results use .show() or .logs() or in CLI: \n",
      "!mlrun get run cfeecfa9880b4047b08685a0e62bddaa --project default , !mlrun logs cfeecfa9880b4047b08685a0e62bddaa --project default\n",
      "[mlrun] 2020-06-08 16:45:45,726 run executed, status=completed\n"
     ]
    },
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "task = new_task(name=\"demo2\", handler=handler, artifact_path=artifact_path).with_params(\n",
    "    p1=7\n",
    ")\n",
    "run = run_local(task)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id=\"run-csv\"></a>\n",
    "______________________________________________"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# **run with hyper parameters taken from a csv file**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
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      "[mlrun] 2020-06-08 16:45:48,437 starting run demo2 uid=14e1be4e4b0f4a42b252cc906b28e329  -> http://mlrun-api:8080\n",
      "> --------------- Iteration: (1) ---------------\n",
      "Run: demo2 (uid=14e1be4e4b0f4a42b252cc906b28e329-1)\n",
      "Params: p1=11, p2=33\n",
      "[mlrun] 2020-06-08 16:45:49,557 log artifact myfig at /User/ml3/mlrun/examples/data/14e1be4e4b0f4a42b252cc906b28e329/1/myfig.html, size: 76607, db: Y\n",
      "[mlrun] 2020-06-08 16:45:49,592 log artifact mydf at /User/ml3/mlrun/examples/data/14e1be4e4b0f4a42b252cc906b28e329/1/mydf.csv, size: 32, db: Y\n",
      "[mlrun] 2020-06-08 16:45:49,614 log artifact mymodel at /User/ml3/mlrun/examples/data/14e1be4e4b0f4a42b252cc906b28e329/1/data/, size: 10, db: Y\n",
      "\n",
      "> --------------- Iteration: (2) ---------------\n",
      "Run: demo2 (uid=14e1be4e4b0f4a42b252cc906b28e329-2)\n",
      "Params: p1=12, p2=44\n",
      "[mlrun] 2020-06-08 16:45:50,741 log artifact myfig at /User/ml3/mlrun/examples/data/14e1be4e4b0f4a42b252cc906b28e329/2/myfig.html, size: 76607, db: Y\n",
      "[mlrun] 2020-06-08 16:45:50,781 log artifact mydf at /User/ml3/mlrun/examples/data/14e1be4e4b0f4a42b252cc906b28e329/2/mydf.csv, size: 32, db: Y\n",
      "[mlrun] 2020-06-08 16:45:50,806 log artifact mymodel at /User/ml3/mlrun/examples/data/14e1be4e4b0f4a42b252cc906b28e329/2/data/, size: 10, db: Y\n",
      "\n",
      "> --------------- Iteration: (3) ---------------\n",
      "Run: demo2 (uid=14e1be4e4b0f4a42b252cc906b28e329-3)\n",
      "Params: p1=13, p2=55\n",
      "[mlrun] 2020-06-08 16:45:51,933 log artifact myfig at /User/ml3/mlrun/examples/data/14e1be4e4b0f4a42b252cc906b28e329/3/myfig.html, size: 76607, db: Y\n",
      "[mlrun] 2020-06-08 16:45:51,974 log artifact mydf at /User/ml3/mlrun/examples/data/14e1be4e4b0f4a42b252cc906b28e329/3/mydf.csv, size: 32, db: Y\n",
      "[mlrun] 2020-06-08 16:45:51,993 log artifact mymodel at /User/ml3/mlrun/examples/data/14e1be4e4b0f4a42b252cc906b28e329/3/data/, size: 10, db: Y\n",
      "\n",
      "[mlrun] 2020-06-08 16:45:52,018 best iteration=3, used criteria max.accuracy\n",
      "[mlrun] 2020-06-08 16:45:52,075 log artifact iteration_results at /User/ml3/mlrun/examples/data/14e1be4e4b0f4a42b252cc906b28e329/iteration_results.csv, size: 167, db: Y\n"
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       "      <td><div title=\"14e1be4e4b0f4a42b252cc906b28e329\"><a href=\"https://mlrun-ui.default-tenant.app.yh55.iguazio-cd2.com/projects/default/jobs/14e1be4e4b0f4a42b252cc906b28e329/info\" target=\"_blank\" >...6b28e329</a></div></td>\n",
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     "text": [
      "to track results use .show() or .logs() or in CLI: \n",
      "!mlrun get run 14e1be4e4b0f4a42b252cc906b28e329 --project default , !mlrun logs 14e1be4e4b0f4a42b252cc906b28e329 --project default\n",
      "[mlrun] 2020-06-08 16:45:52,109 run executed, status=completed\n"
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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "task = new_task(\n",
    "    name=\"demo2\", handler=handler, artifact_path=artifact_path\n",
    ").with_param_file(\"params.csv\", \"max.accuracy\")\n",
    "run = run_local(task)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
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